Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Intelligent process mapping through systematic improvement of heuristics
Journal of Parallel and Distributed Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Learning: A Method for Learning from Examples Under Resource Constraints
IEEE Transactions on Knowledge and Data Engineering
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Noisy Environments
Machine Learning
Learning with Genetic Algorithms: An Overview
Machine Learning
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Optimal sampling of genetic algorithms on polynomial regression
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Differential Evolution with Noise Analyzer
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
On the limitations of adaptive resampling in using the student's t-test evolution strategies
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A differential evolution for optimisation in noisy environment
International Journal of Bio-Inspired Computation
Accumulative sampling for noisy evolutionary multi-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An efficient evolutionary algorithm for solving incrementally structured problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Deriving a robust policy for container stacking using a noise-tolerant genetic algorithm
Proceedings of the 2012 ACM Research in Applied Computation Symposium
An open-source population indifference zone-based algorithm for simulation optimization
Proceedings of the Winter Simulation Conference
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In this paper, we develop new methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment. Such methods are related to the scheduling of resources for tests performed in genetic algorithms. Assuming that the population size is given, we address two problems related to the design of efficient scheduling algorithms specifically important in noisy environments. First, we study the durution-scheduling problem that is related to setting dynamically the duration of each generation. Second, we study the sample-allocation problem that entails the adaptive determination of the number of evaluations taken from each candidate in a generation. In our approach, we model the search process as a statistical selection process and derive equations useful for these problems. Our results show that our adaptive procedures improve the performance of genetic algorithms over that of commonly used static ones.